Neural network apparatus and method of processing variable-resolution operation by the same

ABSTRACT

A neural network apparatus that is configured to process an operation includes neural network circuitry configured to receive a first input of an n-bit activation, store a second input of an m-bit weight, perform a determination whether to perform an operation on an ith bit of the first input and a jth bit of the second input, output an operation value of an operation performed on the ith bit of the first input and the jth bit of the second input based on the determination, and produce an operation value of the operation based on the determination.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a Continuation of U.S. Application No. 16/557,182,filed on Aug. 30, 2019, which claims priority under 35 U.S.C. § 119 toKorean Patent Application No. 10-2019-0012135, filed on Jan. 30, 2019,in the Korean Intellectual Property Office, the disclosures of each ofwhich are incorporated by reference herein in their entirety.

BACKGROUND 1. Field

The present disclosure relates to a neural network apparatus and amethod of processing variable-resolution operation by the neural networkapparatus.

2. Description of the Related Art

There is growing interest in neuromorphic processors that perform neuralnetwork operations. Research has been conducted to realize aneuromorphic processor including neuron circuitry and synapse circuitry.Such a neuromorphic processor may be used as a neural network device todrive various neural networks such as a convolutional neural network(CNN), a recurrent neural network (RNN), and a feedforward neuralnetwork (FNN), and may be used in fields including data classificationand image recognition.

SUMMARY

Some example embodiments may include neural network apparatuses and/ormethods of processing operations by neural network apparatuses.

Additional aspects will be set forth in part in the description whichfollows and, in part, will be apparent from the description, or may belearned by practice of the presented embodiments.

According to an aspect of some example embodiments, a neural networkapparatus that processes a neural network operation includes neuralnetwork circuitry configured to receive a first input corresponding toan i^(th) bit of an n-bit activation, wherein n is a natural number andi is a natural number between 1 and n inclusive; store a second inputcorresponding to a j^(th) bit of an m-bit weight, wherein m is a naturalnumber and j is a natural number between 1 and m inclusive; perform adetermination whether the operation is to be performed on the i^(th) bitand the j^(th) bit; and based on the determination being a positivedetermination, perform the operation on the i^(th) bit and the j^(th)bit, and produce an operation value of the operation performed on thei^(th) bit and the j^(th) bit.

According to some example embodiments, a method of processing aoperation by a neural network apparatus includes determining a firstinput corresponding to an i^(th) bit of an n-bit activation, and asecond input corresponding to a j^(th) bit of an m-bit weight;performing a determination whether an operation is to be performed onthe first input and the second input, and produce a positivedetermination or a negative determination as a result of thedetermination; and performing the operation on the first input and thesecond input to produce an operation value based on the positivedetermination, wherein n and m are natural numbers, i is a naturalnumber between 1 and n inclusive, and j is a natural number between 1and m inclusive.

According to an aspect of some example embodiments, a non-transitorycomputer-readable recording medium may have recorded thereon a programthat, when executed by a computer, performs the method.

BRIEF DESCRIPTION OF THE DRAWINGS

These and/or other aspects will become apparent and more readilyappreciated from the following description of some example embodiments,taken in conjunction with the accompanying drawings in which:

FIG. 1 is a diagram of a neural network according to some exampleembodiments;

FIG. 2 is a diagram for describing a configuration of a two-dimensional(2D) array circuitry for performing a neural network operation;

FIG. 3A is a diagram of symbols for describing components of a neuralnetwork processor included in a neural network apparatus according tosome example embodiments;

FIG. 3B is a diagram of symbols for describing components of neuroncircuitry of neural network processing circuitry according to someexample embodiments;

FIG. 4A is a diagram of an example of neuron circuitry according to someexample embodiments;

FIG. 4B is a diagram of another example of neuron circuitry according tosome example embodiments;

FIG. 5 is a block diagram of a hardware configuration of a neuralnetwork apparatus according to some example embodiments;

FIG. 6 is a diagram for describing inputs of neural network processingcircuitry, according to some example embodiments;

FIG. 7A is a diagram for describing an operation performable by a neuralnetwork apparatus, according to some example embodiments;

FIG. 7B is a diagram for describing mapping of operands to an activationand a weight to perform an operation (e.g., a variable-resolutionmultiplication operation), according to some example embodiments;

FIG. 7C is a diagram for describing processing, by a neural networkprocessor, operands and intermediate products of an operation (e.g., avariable-resolution multiplication operation) in a time-division manner,according to some example embodiments;

FIG. 7D is a diagram for describing virtual synapse array mapping forprocessing an operation by a neural network processor in a time-divisionmanner, according to some example embodiments;

FIGS. 8A and 8B are diagrams for describing an order in which operationsare to be performed by a neural network processor in a time-divisionmanner to process an operation, according to some example embodiments;

FIG. 8C is a diagram for describing in detail a process of performing anoperation by a neural network processor in a time-division manner,according to some example embodiments;

FIG. 9 is a diagram illustrating an example of a process of processing a3-bitx3-bit multiplication operation by a neural network processor,according to some example embodiments;

FIG. 10 is a flowchart of a method of processing an operation by aneural network apparatus, according to some example embodiments; and

FIG. 11 is a block diagram of a configuration of an electronic systemaccording to some example embodiments.

DETAILED DESCRIPTION

In some example embodiments set forth herein, general terms that havebeen widely used nowadays are selected, if possible, in consideration offunctions of some example embodiments, but non-general terms may beselected according to the intentions of technicians in this art,precedents, or new technologies, etc. Furthermore, some terms may bearbitrarily chosen by the present applicant. In this case, the meaningsof these terms will be explained in corresponding example embodiments indetail. Thus, the terms used herein should be defined not based on thenames thereof but based on the meanings thereof and the whole context ofthe example embodiments.

It may be understood that, in a description of some example embodiments,when a part is referred to as being “connected to” another part, thepart is directly connected to the other part or is electricallyconnected to the other part via an element. It will be furtherunderstood that the term “include” or “comprise,” when used herein,specify the presence of stated elements or operations, but do notpreclude the presence or addition of one or more other elements oroperations.

While the terms “first”, “second”, etc., may be used to describe variouselements, the elements should not be construed as being limited by theseterms. These terms are used only to distinguish one element fromanother.

A description of some example embodiments which will be described belowshould not be construed as limiting the scope of the present disclosure,and matters that are obvious to those of ordinary skill in the artshould be construed as being within the scope of some exampleembodiments. Hereinafter, some example embodiments will be described indetail with reference to the accompanying drawings.

As used herein, the term “and/or” includes any and all combinations ofone or more of the associated listed items. Expressions such as “atleast one of,” when previous a list of elements, modify the entire listof elements and do not modify the individual elements of the list.

FIG. 1 is a diagram of a neural network according to some exampleembodiments.

FIG. 1 illustrates an example of a neural network 10 that includes aninput layer, hidden layers, and an output layer. The neural network 10may be configured to perform an operation, based on received input data(e.g., I₁ and I₂), and produce output data (e.g., O₁ and O₂) of anoperation value as a result of performing the operation.

In some example embodiments, the neural network 10 may be a deep neuralnetwork (DNN) or n-layer neural networks including two or more hiddenlayers. For example, as illustrated in FIG. 1 , the neural network 10may be a DNN that includes an input layer Layer 1, two hidden layersLayer 2 and Layer 3, and an output layer Layer 4. Examples of the DNNmay include, but are not limited to, a convolutional neural network(CNN), a recurrent neural network (RNN), a feedforward neural network(FNN), deep belief networks, and restricted Boltzman machines.

Although FIG. 1 illustrates that the neural network 10 includes fourlayers, some example embodiments are not limited thereto and the neuralnetwork 10 may include less than or more than four layers.Alternatively, the neural network 10 may include various types of layersdifferent from those of FIG. 1 .

Each of the layers illustrated in the neural network 10 may include aplurality of artificial neurons known as “neurons”, “processing elements(PEs)”, “units”, “nodes”, or terms similar thereto. For example, asillustrated in FIG. 1 , the input layer Layer 1 may include two neurons,and the hidden layer Layer 2 may include three neurons. The number ofneurons of each of the layers Layer 1 and Layer 2 are merely an example,and the layers of the neural network 10 may include various numbers ofneurons.

The neurons included in each of the layers of the neural network 10 maybe connected to each other to exchange data. For example, one neuron maybe configured to perform an operation by receiving data from neurons ofa previous layer and to produce an operation value by performing theoperation to neurons of a subsequent layer.

An output of each neuron may be referred to as an activation. Theactivation may be an operation value of one neuron and may be also aninput value of neurons included in a subsequent layer. Each neuron mayidentify an activation thereof, based on activations received fromneurons included in a previous layer and weights. A weight is aparameter used to calculate an activation of each neuron and may be avalue allocated to a correlation between neurons. The weight may bestored at a synapse connecting neurons.

Each neuron may be a computational unit that is configured to receive aninput and produce an operation value, such as an activation, and to mapan input and an operation value or output to each other. For example,when σ is an activation function,

w_(jk)^(i)

is a weight assigned from a k^(th) neuron included in an (i-1)^(th)layer to a j^(th) neuron included in an i^(th) layer,

b_(j)^(i)

is a bias of the j^(th) neuron included in the i^(th) layer, and

a_(j)^(i)

is an activation of the j^(th) neuron included in the i^(th) layer, anactivation

a_(j)^(I)

may be calculated by Equation 1 below.

$a_{j}^{i} = \sigma({\sum\limits_{k}{w_{jk}^{i} \times a_{k}^{i - 1}) + b_{j}^{i})}}$

As described above, an example neural network operation may include amultiplication operation of multiplying an output value of a neuron of aprevious layer and a weight of a synapse, and an addition operation ofadding results of multiplications at a receiving neuron.

FIG. 2 is a diagram for describing a configuration of two-dimensional(2D) array circuitry for performing a neural network operation.

FIG. 2 illustrates a configuration of 2D array circuitry 20 forperforming the neural network operation described above with referenceto FIG. 1 . The configuration of the 2D array circuitry 20 includes Naxon circuits A₁ to A_(N) 210, M neuron circuits N₁ to N_(M) 230, and anNxM synapse array S₁₁ to S_(NM) 220, wherein N and M are each naturalnumbers.

Synapses of the respective synapse array S₁₁ to S_(NM) 220 may bearranged at intersections of first-direction lines extending in a firstdirection from the axon circuits A₁ to A_(N) 210 and second-directionlines extending in a second direction from the neuron circuits N₁ toN_(M) 230. Here, it is illustrated that the first direction is a rowdirection and the second direction is a column direction for convenienceof explanation, but the first and second directions are not limitedthereto, and the first direction may be a column direction and thesecond direction may be a row direction.

The axon circuits A₁ to A_(N) 210 may receive activations (e.g.,activations a₁, a₂, ..., a_(N)) and transmit the activations to thefirst-direction lines. The activations correspond to neurotransmitterstransmitted via neurons, and may be electric signals input to the axoncircuits A₁ to A_(N) 210. Each of the axon circuits A₁ to A_(N) 210 mayinclude a memory, register, or buffer storing input information. Theactivations may be binary activations each having a binary value. Forexample, a binary activation may include 1-bit information correspondingto a logic value 0 or 1. However, the activations are not limitedthereto, and may each be a multi-bit value.

Each of the synapses of the synapse array S₁₁ to S_(NM) 220 may store aweight corresponding to strength of a connection between neurons. InFIG. 2 , for convenience of explanation, w₁, w₂, ..., w_(m) areillustrated as examples of weights to be stored at synapses, but otherweights may further be stored at the synapses. Each of the synapses ofthe synapse array S₁₁ to S_(NM) 220 may include a memory device to storea weight or may be connected to another memory device storing a weight.Here, such a memory device may be, for example, a memrister. The weightmay be a binary weight that is a binary value. For example, a binaryweight may include 1-bit information corresponding to a logic value 0or 1. However, the weight is not limited thereto, and may be a multi-bitvalue.

Each of the synapses of the synapse array S₁₁ to S_(NM) 220 may receivean activation transmitted from one of the axon circuits A₁ to A_(N) 210via a corresponding first-direction line and produce an operation valueof a neural network operation performed between the stored weight andthe activation. For example, the neural network operation performedbetween the weight and the activation may be a multiplication operation(e.g., an AND operation), but is not limited thereto. The operationvalue of the neural network operation performed between the weight andthe activation may be a value produced through another appropriateoperation for simulating a strength or size of the activation adjustedaccording to strength of a connection between neurons. A magnitude orintensity of a signal transmitted from the axon circuits A₁ to A_(N) 210to the neuron circuits N₁ to N_(M) 230 may be adjusted, based on theneural network operation performed between the weight and theactivation.

Each of the neuron circuits N₁ to N_(M) 230 may receive the operationvalue of the neural network operation performed between the weight andthe activation via a corresponding second-direction line. Each of theneuron circuits N₁ to N_(M) 230 may determine whether to output a spike,based on the operation value of the neural network operation. Forexample, each of the neuron circuits N₁ to N_(M) 230 may output a spikewhen a value of accumulated results of the neural network operation isequal to or greater than a predetermined threshold. The spikes outputfrom the neuron circuits N₁ to N_(M) 230 may correspond to an activationto axon circuits of a next stage.

FIG. 3A is a diagram of symbols for describing components of neuralnetwork processing circuitry 30 that may be included in a neural networkapparatus according to some example embodiments. FIG. 3A, as well asother included herein (such as FIG. 3B, FIG. 4A, FIG. 4B, FIG. 5 ,etc.), is presented as an illustration of an example embodiment. It isto be appreciated that the example embodiment depicted in each figureincludes an example organization of the neural network apparatus, andthat other example embodiments may feature a different organization,such as more or fewer components (including neuron circuitry configuredto perform some or all of the operations), or a different arrangement ofcomponents to carry out the operations of the neural network apparatus.A variety of example embodiments organized in a same, similar, ordifferent manner as the example embodiment of FIG. 3 may be devised, andall such example embodiments and variations thereof may be consistentwith the present disclosure. The scope of the present disclosure is tobe determined only by the claims appended hereto.

Some example embodiments, including example embodiments shown in thefigures, may include various forms of circuitry, including (for example)neural network processing circuitry; neuron circuitry; axon circuitry;synapse circuitry; neuron circuitry; adder circuitry; control circuitry;and controller circuitry. It is to be appreciated that these and otherforms of circuitry may include hardware such as logic circuits; ahardware/software combination, such as a processor executing software;or a combination thereof. For example, a processor may include, but isnot limited to, a central processing unit (CPU), an arithmetic logicunit (ALU), a digital signal processor, a microcomputer, a fieldprogrammable gate array (FPGA), a System-on-Chip (SoC), a programmablelogic unit, a microprocessor, application-specific integrated circuit(ASIC), etc.

Referring to FIG. 3A, an example embodiment of the neural networkprocessing circuitry 30 may include axon circuitry 310, synapsecircuitry 320, and neural network circuitry 330. In this exampleembodiment, the synapse circuitry 320 may be configured to receiveweights from an NxM synapse memory array 325 included in an externalmemory device. The configuration of the 2D (NxM) array circuitry 20described above with reference to FIG. 2 may be embodied using the axoncircuitry 310, the synapse circuitry 320, and the neural networkcircuitry 330 of FIG. 3A. The axon circuits A₁ to A_(N) 210 of FIG. 2may correspond to the axon circuitry 310, the synapse array S₁₁ toS_(NM) 220 may correspond to the synapse circuitry 320, and the neuroncircuits N₁ to N_(M) 230 may correspond to the neural network circuitry330.

The axon circuitry 310 of the neural network processing circuitry 30 maybe configured to process activations in a time-division manner so thatthe axon circuitry 310 may operate similar to the axon circuits A₁ toA_(N) 210 of FIG. 2 . Similarly, in order to operate like the synapsearray S₁₁ through S_(NM) 220 of FIG. 2 , the synapse circuitry 320 ofthe neural network processing circuitry 30 may be configured to storeweights in the time-division manner so that the synapse circuitry 320may be configured to operate similar to the synapse array S₁₁ to S_(NM)220 of FIG. 2 .

In some example embodiments, the axon circuitry 310 may be configured tooperate as the axon circuitry A₁ of FIG. 2 at a point in time t₁,operate as the axon circuitry A₂ of FIG. 2 at a point of time t₂, andoperate as the axon circuitry A_(N) of FIG. 2 at a point of time t_(N).The synapse circuitry 320 may be configured to operate as the synapseS₁₁ of FIG. 2 at a point of time t₁₋₁, operate as the synapse S₁₂ ofFIG. 2 at a point of time t₁₋₂, and operate as the synapse S_(NM) ofFIG. 2 at a time of time = t_(N-M). The neural network circuitry 330 mayalso be configured to operate as the neuron circuits N₁ to N_(M) 230 ofFIG. 2 in the same manner. Here, the points of time are all points oftime and are denoted by different reference numerals to bedistinguishable. As such, when each of the axon circuitry 310, thesynapse circuitry 320, and the neural network circuitry 330 operates atone of certain points in time in the time-division manner, (1x1)circuitry may be embodied as operating as if a plurality of (NxM)circuits operate. For example, the 2D (NxM) array circuitry 20 of FIG. 2may be embodied as the neural network processing circuitry 30 of (1x1)circuits by operating each circuitry of the 2D (NxM) array circuitry 20in the time-division manner.

In some example embodiments, the neural network processing circuitry 30may be capable of processing not only a 1-bit operation but also avariable-resolution operation, which may permit a selection of a numberof bits between 1 and a larger integer, by appropriately controlling apoint of time at which each of the (1 x1) circuits will operate in atime-division manner. For example, the neural network processingcircuitry 30 may be configured to perform an operation between an n-bitactivation (here, n is a natural number) and an m-bit weight (here, m isa natural number). In this case, the axon circuitry 310 of the neuralnetwork processing circuitry 30 may receive a first input correspondingto an i^(th) bit of the n-bit activation at a certain point of time(here, i is a natural number greater than or equal to 1 and equal to orless than n), and the synapse circuitry 320 of the neural networkprocessing circuitry 30 may be configured to store a second inputcorresponding to a j^(th) bit of the m-bit weight (here, j is a naturalnumber greater than or equal to 1 and equal to or less than m) andproduce an operation value of an operation performed between the firstand second inputs at a certain point of time. For example, the value ofthe operation performed between the first and second inputs may be avalue obtained by multiplying the first and second bits but is notlimited thereto.

In some example embodiments, the neural network circuitry 330 of theneural network processing circuitry 30 may be configured to obtainvalues of the operation performed between the n-bit activation and them-bit weight by performing the addition operation on, as inputs, theoperation value from the synapse circuitry 320. The neural networkcircuitry 330 will be described in more detail below with reference toFIG. 3B. However, it is to be appreciated that some example embodimentsmay include other forms of processing, including other basic arithmeticoperations including subtraction, multiplication, and/or division;advanced arithmetic operations such as linear algebra and/or vectoroperations; and/or logical but not necessarily arithmetic operations,such as a pooling operation (e.g., avgpool or maxpool) of aconvolutional neural network layer.

FIG. 3B is a diagram of symbols for describing components of neuralnetwork circuitry 330 of a neural network processing circuitry 30,according to some example embodiments.

Referring to FIG. 3B, some example embodiments of the neural networkcircuitry 330 of the neural network processing circuitry 30 may includecontrol circuitry 331, adder circuitry 332, shift registers 333 and 334,and a comparator 335. However, some example embodiments may include adifferent set of components than the components illustrated in FIG. 3Bof the neural network circuitry 330. For example, in some exampleembodiments, the neural network circuitry 330 may further includecomponents other than the components of FIG. 3B or may include only someof the components of FIG. 3B.

In some example embodiments, The control circuitry 331 may be circuitryconfigured to determine whether the operation is to be performed on, asan input, at least one of values of an operation output from the synapsecircuitry 320 to produce an operation value of an operation between ann-bit activation and an m-bit weight, and to output the operation value,wherein the producing the operation value and the outputting theoperation value are based on the determination.

For example, the control circuitry 331 may perform the determinationbased on whether the value of the operation is influenced by anoperation value of the operation (e.g., a preceding instance of theoperation) performed on, as an input, at least one of the values of theoperation output from the synapse circuitry 320, and the determinationmay be a negative determination when the operation value of theoperation is not influenced by the operation value of a precedinginstance of the operation.

In some example embodiments, the neural apparatus may include an addercircuitry 332 as combination circuitry having three inputs (e.g., anaugend A, an addend B, and/or a previous carry digit C₀) and/or twooutputs (e.g., non-carry sum S₁ and/or a carry digit C₁). In someexample embodiments, the adder circuitry 332 may correspond to a fulladder. When receiving a positive determination (such as an enablesignal) from the control circuitry 331, the adder circuitry 332 may beconfigured to perform the addition operation and obtain one of bitsrepresenting values of the operation. Some example embodiments in whicha value of the addition operation performed by the adder circuitry 332may correspond to one of the bits representing the operation value ofthe operation will be described in more detail below with reference toFIGS. 6 to 8C.

When the determination by the control circuitry 331 is a negativedetermination, the adder circuitry 332 may be configured to skip theaddition operation. The negative determination from the controlcircuitry 331 may be determined when the operation value of the additionoperation may not be influenced by an operation value of a precedinginstance of the addition operation, and thus the adder circuitry 332 maybe configured to refrain from performing an unnecessary additionoperation that may not have an influence on another instance of theaddition operation. Accordingly, in some example embodiments, powerconsumption for the adder circuitry 332 to perform the additionoperation may be reduced, and total efficiency of the neural networkoperation may be increased due to skipping of unnecessary operations.Alternatively or additionally, in some example embodiments, theoperation of the neural network circuitry may complete faster, such asby reducing memory access and/or expediting the completion of logicaland/or arithmetic processing. Faster processing may enable, for example,more rapid completion of a neural network training or retrainingprocess; an undertaking a more complex or comprehensive neural networktraining or retraining process, which may involve more logicaloperations within a time frame; and/or more rapid inference of a trainedneural network, which may be advantageous, for example, in realtime orotherwise time-sensitive machine learning scenarios, such as robotics,industrial processing systems, and/or autonomous vehicles.

Example embodiments of the neural network circuitry 330 that include thecontrol circuitry 331 will be described below with reference to FIGS. 4Aand 4B.

FIG. 4A is a diagram of an example of neuron circuitry according to someexample embodiments. FIG. 4A illustrates an example embodiment in whichthe control circuitry 331 of the neural network circuitry 330 includesgate circuitry 410. The gate circuitry 410 may be configured to receiveat least some of a predetermined initial value, a value of an operationoutput from preceding instance of the operation, for example, producedby the synapse circuitry 320 at a previous point of time, a value of theoperation output from the synapse circuitry 320 at a current point oftime, a value of addition processed by the adder circuitry 332 from apreceding instance of the operation, for example, at the previous pointof time, and a carry digit determined by the adder circuitry 332 from apreceding instance of the operation, for example, at the previous pointof time. The predetermined initial value or the carry digit determinedby the adder circuitry 332 at the previous point of time may correspondto C₀ of FIG. 4A. A value among the value of the operation output fromthe synapse circuitry 320 at the previous point of time, the value ofthe operation output from the synapse circuitry 320 at the current pointof time, and the value of addition processed by the adder circuitry 332at the previous point of time may correspond to A or B of FIG. 4A.

The gate circuitry 410 may be configured to output a negativedetermination (such as a disable signal) when all the received values ofa set of received values (e.g., the values C₀, A and B) are equal to afirst value and to output a positive determination (such as an enablesignal) when any one of the received values of the set of receivedvalues is equal to a second value. For example, the first value may be“0” and the second value may be “1”. The gate circuitry 410 may be an ORgate that outputs “0” as the negative determination (such as the disablesignal) when all the received values are “0” and outputs “1” as thepositive determination (such as the enable signal) when any one of thereceived values is “1”. In some example embodiments, the adder circuitry332 may include an enable terminal, and a determination (such as asignal) produced by the gate circuitry 410 may be input to the enableterminal of the adder circuitry 332. Further, some example embodimentsmay be configured to produce an enable or disable signal to connote thepositive determination and the negative determination, while otherexample embodiments may be configured to utilize the determination toaffect the processing of one or more bits of the operands directly andwithout producing an enable signal or a disable signal.

The adder circuitry 332 may be configured to perform the additionoperation when the positive determination (such as the enable signal, orthe value “1”) is received by the enable terminal of the adder circuitry332 and to skip the addition operation when the negative determination(such as the disable signal, or the value “0”) is received by the enableterminal of the adder circuitry 332. The skipping of the additionoperation performed by the adder circuitry 332 may be understood to meanthat the adder circuitry 332 is not operated and power consumption fordriving the adder circuitry 332 is reduced.

FIG. 4B is a diagram of another example of neuron circuitry according tosome example embodiments.

FIG. 4B illustrates an example embodiment in which control circuitry 331includes gate circuitry 420 that may be configured differently than thatof FIG. 4A. For example, the gate circuitry 420 may be a NOR gate thatoutputs “1” when all received values of a set of received values (e.g.,values C₀, A and B) are equal to a first value (e.g., “0”), and outputs“0” when any one of the received values is equal to a second value(e.g., “1”). In this case, the adder circuitry 332 may include a disableterminal or a ‘NOT’ enable terminal other than the enable terminal, sothat “1” output from the gate circuitry 420 may be used as a negativedetermination (such as a disable signal) and “0” output from the gatecircuitry 420 may be used as a positive determination (such as an enablesignal).

The adder circuitry 332 may be configured to perform the additionoperation when the positive determination (such as the enable signal orvalue “0”) is received by the disable terminal or the ‘NOT’ enableterminal of the adder circuitry 332 and to skip the addition operationwhen the negative determination (such as the disable signal or value“1”) is received by the disable terminal or the ‘NOT’ enable terminal ofthe adder circuitry 332. FIGS. 4A and 4B illustrate cases in which thecontrol circuitry 331 includes the gate circuitry 410 or 420 but thecases are merely example. Alternatively, the control circuitry 331 mayinclude circuitry configured to determine an operation to be performedby the adder circuitry 332 is not necessary and output the positivedetermination (such as the enable signal) or the negative determination(such as the disable signal) as a result of the determination.

Referring back to FIG. 3B, the shift registers 333 and 334 mayrespectively store a value of addition and a carry bit output from theadder circuitry 332. Furthermore, the shift registers 333 and 334 mayshift the value of addition and the carry bit stored therein by aspecified number of bits to adjust the number of bits in calculation ofbits representing an operation value of the operation. Each of the shiftregisters 333 and 334 may have a double latch structure to transmit apredetermined initial value (e.g., the first value or “0”) as an inputagain to the adder circuitry 332, so that the addition operation may besmoothly performed at a subsequent point of time when the additionoperation to be performed by the adder circuitry 332 at a current pointof time is skipped.

The comparator 335 compares a result C₁S₁ of addition performed by theadder circuitry 332 with a predetermined threshold. Here, thepredetermined threshold is a criterion for determining whether a spikeis to be output to a subsequent neuron. When the comparison of thecomparator 335 reveals that the result C₁S₁ of addition is greater thanor equal to the predetermined threshold, the neural network circuitry330 may output a spike. In some example embodiments, the comparator 335may be configured to compare only the result C₁S₁ of addition performedby the adder circuitry 332 with the predetermined threshold at a certainpoint of time corresponds to some example embodiments in which a 1-bitoperation is performed. When the comparator 335 performs the operation,a result of addition to be compared with the threshold may beaccumulated results of the addition operation performed by the addercircuitry 332.

When the addition operation to be performed by the adder circuitry 332may be skipped by the control circuitry 331, the neural networkcircuitry 330 may be configured to determine a result of addition and acarry digit, which would be obtained from the adder circuitry 332 unlessthe addition operation were skipped, to the first value (e.g., “0”). Forexample, the neural network circuitry 330 may be configured to store “0”in the shift registers 333 and 334 when the addition operation to beperformed by the adder circuitry 332 is skipped. “0” stored in the shiftregisters 333 and 334 may be used in the addition operation of the addercircuitry 332 at a subsequent point of time, or may be determined to beone of bits representing an operation value of the operation.Hereinafter, a hardware configuration of a neural network apparatus withneural network processing circuitry 30 will be described with referenceto FIG. 5 , and a method of processing the operation by the neuralnetwork apparatus will be described in more detail with reference toFIGS. 6 to 8C.

FIG. 5 is a block diagram of a hardware configuration of a neuralnetwork apparatus 100 according to some example embodiments.

Referring to FIG. 5 , the neural network apparatus 100 includes neuralnetwork circuitry 110 on which neural network processing circuitry 112and an on-chip memory 114 are mounted, and an external memory 120. Theneural network processing circuitry 112 includes axon circuitry 1121,synapse circuitry 1122, neuron circuitry 1123, and controller circuitry1124. However, FIG. 5 only illustrates components of the neural networkapparatus 100 related to an example embodiment. Thus, it would beobvious to one of ordinary skill in the art that the neural networkapparatus 100 may further include other general-purpose components, suchas a central processing unit (CPU), a graphics processing unit (GPU), anapplication processor (AP), a sensor module, and a communication module,in addition to the components shown in FIG. 5 .

In some example embodiments, the neural network processing circuitry 112may correspond to the neural network processing circuitry 30 of FIG. 3A,the axon circuitry 1121 corresponds to the axon circuitry 310 of FIG.3A, the synapse circuitry 1122 corresponds to the synapse circuitry 320of FIG. 3A, and the neuron circuitry 1123 corresponds to the neuralnetwork circuitry 330 of FIG. 3A.

The neural network processing circuitry 112 may include a processingunit (or processor core) embodied similar to the neural networkprocessing circuitry 30 of FIG. 3A, but is not limited thereto and theneural network processing circuitry 112 may include a plurality ofprocessing units (or processor cores) each embodied as the neuralnetwork processing circuitry 30 of FIG. 3A.

The neural network apparatus 100 may be an apparatus included in varioustypes of electronic devices, such as a personal computer (PC), a serverdevice, a mobile device, and an embedded device. The neural networkapparatus 100 may correspond to a hardware component included in a smartphone, a tablet device, an augmented reality (AR) device, anInternet-of-Things (IoT) device, an autonomous vehicle, a robotics, or amedical device that performs voice recognition, image recognition, imageclassification, etc. using a neural network. That is, the neural networkapparatus 100 may correspond to an exclusive hardware (HW) acceleratormounted in such an electronic device and may be, but is not limited to,an HW accelerator operating like a neural processing unit (NPU), atensor processing unit (TPU), a neural engine, TrueNorth, or Loihi,which may be an exclusive module for neural network driving.

The neural network circuitry 110 may be configured to control overallfunctions for driving a neural network in the neural network apparatus100. For example, the neural network processing circuitry 112 of theneural network circuitry 110 performs overall control of the neuralnetwork apparatus 100 by accessing neural network data (for example,activations, weights, etc.) stored in the external memory 120 of theneural network apparatus 100 to execute neural network-related programs.The neural network circuitry 110 may drive the neural network undercontrol of a CPU, a GPU, an AP, or the like provided inside or outsidethe neural network apparatus 100.

The external memory 120 is hardware storing various types of neuralnetwork data processed in the neural network circuitry 110, and maystore data processed or to be processed by the neural network circuitry110. Furthermore, the external memory 120 may store applications,drivers, etc. to be driven by the neural network circuitry 110. Theexternal memory 120 may include random access memory (RAM), such asdynamic random access memory (DRAM) or static random access memory(SRAM), read-only memory (ROM), electrically erasable programmableread-only memory (EEPROM), a CD-ROM, Blue-ray or another optical diskstorage, a hard disk drive (HDD), a solid-state drive (SSD), or a flashmemory.

The on-chip memory 114 of the neural network circuitry 110 may readneural network data (activations, weights, etc.), for pre-synapticneuron circuits, from the external memory 120, store (or buffer) theneural network data, and execute the neural network by using the storedneural network data. For example, the NxM synapse memory array 325 ofFIG. 3A may correspond to the on-chip memory 114. The on-chip memory 114may store data for post-synaptic neuron circuits, such as operationvalues of neural network operations produced as results of execution ofthe neural network, spike values, etc.

Operations and functions of the axon circuitry 1121, the synapsecircuitry 1122, the neuron circuitry 1123, and the controller circuitry1124 of the neural network processing circuitry 112 will be describedbelow with reference to other drawings.

FIG. 6 is a diagram for describing inputs of each circuitry of neuralnetwork processing circuitry 112, according to some example embodiments.

Referring to FIG. 6 , the controller circuitry 1124 of the neuralnetwork processing circuitry 112 determines a first input correspondingto an i^(th) bit of an n-bit activation 610 to be assigned to the axoncircuitry 1121 at each point of time, and a second input of a j^(th) bitof an m-bit weight 620 to be assigned to the synapse circuitry 1122 ateach point of time. Here, n and m are each a natural number, i is anatural number between 1 and n inclusive, and j is a natural numberbetween 1 and m inclusive.

In FIG. 6 , it is assumed that for convenience of expression, the n-bitactivation 610 and the m-bit weight 620 are each a 3-bit value (n=3 andm=3). However, the n-bit activation 610 and the m-bit weight 620according to some example embodiments may not be limited thereto and maybe various-bit values.

The controller circuitry 1124 may be configured to determine a bit valueof which a bit position (e.g., the i^(th) bit) on the n-bit activation610 (n=3) is to be assigned as the first input to the axon circuitry1121 at a certain point of time (e.g., a point of time t_(x)). That is,the controller circuitry 1124 determines i to determine the first inputto be assigned at the point of time t_(x). For example, i may be a valueranging from 1 to 3. When i is 1, the first input of a first bitcorresponds to a least significant bit (LSB) of the n-bit activation610, and when i is 3, the first input of a third bit corresponds to amost significant bit (MSB) of the n-bit activation 610.

Furthermore, the controller circuitry 1124 may be configured todetermine a bit value of which a bit position (e.g., the j^(th) bit) onthe m-bit weight 620 (m=3) is to be assigned as the second input to thesynapse circuitry 1122 at a certain point of time (for example, a timet_(y)). That is, the controller circuitry 1124 determines j to determinethe second input to be assigned at the point of time t_(y). For example,j may be a value ranging from 1 to 3. When j is 1, the second input of afirst bit corresponds to an LSB of the m-bit weight 620, and when j is3, the second input of a third bit corresponds to an MSB of the m-bitweight 620. It is to be appreciated that some example embodiments mayutilize other forms of processing; for example, the inputs may beformatted in MSB order, or may be formatted in LSB order, whereincounters i, j may be decremented from n, m down to 1.

As such, the controller circuitry 1124 repeatedly determines the firstinput and the second input to be assigned respectively to the axoncircuitry 1121 and the synapse circuitry 1122 by choosing the values iand j at each point of time until a lower bit value to an upper bitvalue of an operation value of an operation finally output from theneural network processing circuitry 112 are produced. Here, the lowerbit value may be an LSB of the operation value of the operation and theupper bit value may be an MSB of the operation value of the operation.The terms “first input” and “second input” should be understood to meanvalues of bit positions determined by the controller circuitry 1124, andrefer to values newly updated by the controller circuitry 1124 at eachpoint of time.

The controller circuitry 1124 may map i and j such that the i^(th) bitand the j^(th) bit are differently combined at each point of time. Forexample, the controller circuitry 1124 may choose and map the values iand j, such that combinations from a combination of the i^(th) bit(e.g., an LSB) and the j^(th) bit (e.g., an LSB) mapped such that thesum of i and j is smallest to a combination of the i^(th) bit (e.g., anMSB) and the j^(th) bit (e.g., an MSB) mapped such that the sum of i andj is largest are sequentially assigned to each of the axon circuitry1121 and the synapse circuitry 1122. Here, the total number ofcombinations of the i^(th) bit and the j^(th) bit corresponds to aproduct of n and m, e.g., a total of 9(=3x3) combinations in the exampleof FIG. 6 . For example, the controller circuitry 1124 may initially mapa bit value (first input) of an LSB (i=1) of the n-bit activation 610and a bit value (second input) of an LSB (j=1) of the m-bit weight 620at an initial time, and lastly map a bit value (first input) of an MSB(i=3) of the n-bit activation 610 and a bit value (second input) of anMSB (j=3) of the m-bit weight 620 at a last time.

The axon circuitry 1121 of the neural network processing circuitry 112receives the first input of the i^(th) bit of the n-bit activation 610determined by the controller circuitry 1124. The synapse circuitry 1122of the neural network processing circuitry 112 stores the second inputof the j^(th) bit of the m-bit weight 620 determined by the controllercircuitry 1124.

The axon circuitry 1121 and the synapse circuitry 1122 are circuitscapable of processing a bit value (e.g., a 1-bit value). Accordingly,the axon circuitry 1121 and the synapse circuitry 1122 are capable ofrespectively processing only a bit value (first input) of a certainposition on the n-bit activation 610 and a bit value (second input) of acertain position on the m-bit weight 620.

When the second input of the j^(th) bit is stored in the synapsecircuitry 1122, the synapse circuitry 1122 outputs a value of anoperation performed between the first input received from the axoncircuitry 1121 and the second input stored in the synapse circuitry1122. The operation performed by the synapse circuitry 1122 may be amultiplication operation (e.g., an AND operation) performed on the firstand second inputs but is not limited thereto.

The neuron circuitry 1123 of the neural network processing circuitry 112obtains each bit value of the operation value of the operation performedbetween the n-bit activation 610 and the m-bit weight 620, based on theoperation value of the operation produced by the synapse circuitry 1122,as will be described in detail below with reference to a correspondingdrawing.

FIG. 7A is a diagram for describing a operation performable by a neuralnetwork apparatus, according to some example embodiments.

Referring to FIG. 7A, a multiplication operation between 3-bit binaryvalues is illustrated as an example of the variable-resolutionoperation. In the multiplication operation, at least one of operandsincludes a selectable number of bits, which may be selectable between arange between 1 and a larger integer. When a first operand 711 of themultiplication operation is ABC₂ that is a 3-bit binary value and asecond operand 712 is DEF₂ that is a 3-bit binary value, amultiplication operation of the first operand 711 and the second operand712 may be performed by calculating intermediate products throughbitwise multiplication and thereafter adding the calculated intermediateproducts according to the same bit positions.

In detail, GHI 713 that is a first intermediate product is obtained bymultiplying F that is an LSB of DEF₂ that is the second operand 712 andthe first operand ABC₂ 711, JKL 714 that is a second intermediateproduct is obtained by multiplying E that is a second bit of the secondoperand DEF₂ 712 and the first operand ABC₂ 711, and MNO 715 that is athird intermediate product is obtained by multiplying D that is a thirdbit (e.g., an MSB) of the second operand DEF₂ 712 and the first operandABC₂ 711. Thereafter, the first to third intermediate products 713 to715 are added according to the same bit positions to obtain PQRSTU₂ thatis a result 716 of the multiplication operation result performed betweenthe first operand ABC₂ 711 and the second operand DEF₂ 712. A method ofperforming the operation (e.g., the multiplication operation) of FIG. 7Aby the neural network processing circuitry 112 of FIG. 5 will bedescribed below.

FIG. 7B is a diagram for describing mapping of operands to an activationand a weight to perform an operation (e.g., a multiplication operation),according to some example embodiments.

Referring to FIG. 7B, bit values A, B, and C of the first operand ABC₂711 of FIG. 7A are mapped to a weight 720. An MSB of the first operandABC₂ 711 is mapped to an MSB of the weight 720 and an LSB thereof ismapped to an LSB of the weight 70.

In the same manner, bit values D, E, and F of the second operand DEF₂712 of FIG. 7A are mapped to an activation 710. An MSB of the secondoperand DEF₂ 712 is mapped to an MSB of the activation 710 and an LSBthereof is mapped to an LSB of the activation 710.

However, some example embodiments may not be limited thereto. Forexample, as shown in the example embodiment of FIG. 7A, the firstoperand ABC₂ 711 and the second operand DEF₂ 712 of FIG. 7A may bemapped to bit positions on the activation 710 and the weight 720 in adifferent mapping method, unlike the mapping method described above withreference to FIG. 7B. However, when the different mapping method isapplied, the controller circuitry 1124 may determine combinations of iand j to correspond to the mapped bit positions in a manner differentthan that described above.

FIG. 7C is a diagram for describing processing, by a neural networkprocessor, operands and intermediate products of an operation (e.g., avariable-resolution multiplication operation) in a time-division manner,according to some example embodiments.

Referring to FIG. 7C, as described above with reference to FIG. 7B, bitvalues A, B, and C of ABC₂ that is a first operand 731 are mapped to theweight 720 and bit values D, E, and F of DEF₂ that is a second operand732 are mapped to the activation 710.

Bit values of each of a first intermediate product GHI 733, a secondintermediate product JKL 734, and a third intermediate product MNO 735,which are produced through multiplication of the bit values A, B, and Cof the first operand ABC₂ 731 and the bit values D, E, and F of thesecond operand DEF₂ 732, may be obtained in the time-division manner. Indetail, a bit value I of the first intermediate product GHI 733 may beobtained at a point of time t₀, a bit value H of the first intermediateproduct GHI 733 may be obtained at a point of time t₁, a bit value L ofthe second intermediate product JKL 734 may be obtained at a point oftime t₂, and a bit value M of the third intermediate product MNO 635 maybe obtained at a time point of t₈.

The points of time t₀ through t₈ may be different times, which maycorrespond to respective instances of performing the operation on bitsof the operands. For example, a time delayed by a predetermined timefrom the point of time t₀ may be the point of time t₁, and a timedelayed by a predetermined time from the point of time t₁ may be thepoint of time t₂. However, some example embodiments may not be limitedthereto. Throughout the specification, a point of time t is not intendedto indicate a specific moment, but for distinguishing timing or a timesection when related operations are performed. Accordingly, it would beobvious to one of ordinary skill in the art that operations describedherein to be performed at a specific point of time may not benecessarily simultaneously performed. For example, in some exampleembodiments, multiple bits of the operands may be processedconcurrently, and the processing of a bit of the operands may affect(e.g., may result in a continuation and/or cessation of) concurrentprocessing of another bit of the operands. Some example embodiments mayprocess bits of the operands in part concurrent and in partsequentially, such as where a processing of a first bit only partlyoverlaps a processing of a second bit, or where a bit is processedconcurrently with another bit and sequentially with yet another bit.

An operation value 636 of the operation (e.g., a variable-resolutionmultiplication operation) performed on the first operand ABC₂ 731 andthe second operand DEF₂ 732 is PQRSTU₂.

U, which is an LSB of the result PQRSTU₂ 636, is obtained using I of thefirst intermediate product GHI 633. T of the result PQRSTU₂ 636 isobtained from the sum of H of the first intermediate product GHI 733 andL of the second intermediate product JKL 734. S of the result PQRSTU₂736 is obtained from the sum of G of the first intermediate product GHI733, K of the second intermediate product JKL 734, O of the thirdintermediate product MNO 735, and a carry value obtained from a previousbit position. In this way, the result PQRSTU₂ 736 may be sequentiallyobtained from U corresponding to the LSB to P corresponding to the MSB.In other words, the result PQRSTU₂ 736 may be obtained based on the sumof bit values of the first through third intermediate products 733through 735 sequentially obtained at the point of time t₀ to the pointof time t₈.

FIG. 7D is a diagram for describing virtual synapse array mapping forprocessing an operation by a neural network processor in a time-divisionmanner, according to some example embodiments.

Referring to FIG. 7D, as described above, the neural network processingcircuitry 112 includes the axon circuitry 1121, the synapse circuitry1122, the neuron circuitry 1123, and the controller circuitry 1124, butwhen the neural network processing circuitry 112 operates in thetime-division manner, the neural network processing circuitry 112 mayoperate as if a 2D synapse array including neuron circuitry, synapsecircuitry, and/or neuron circuitry, is processed. As shown in theexample embodiment of FIG. 7D, a 2D synapse array simulated to beprocessed by the neural network processing circuitry 112 in thetime-division manner is referred to as a virtual synapse array 740, butis not limited thereto and may be alternately referred to as anotherterm. In other words, in some example embodiments, physical circuitryconfigurations (for example, at least one synapse circuitry and at leastone neuron circuit) of the virtual synapse array 740 may not beimplemented by the neural network processing circuitry 112.

FIG. 7D illustrates the 3x3 virtual synapse array 740 to be described inrelation to the operation (the 3-bitx3-bit multiplication operation)described above with reference to FIGS. 7A through 7C. However,according to the example embodiment shown in FIG. 7D, an array includingvarious numbers of rows and columns may be used.

In the 3x3 virtual synapse array 740, the second operand DEF₂ 732 ofFIG. 7C is mapped to activations a₃, a₂, and a₁, and the first operandABC₂ 731 of FIG. 7C is mapped to weights w₃, w₂, and w₁. The bit value Iof the first intermediate product GHI 733 of FIG. 7C may be obtained byperforming an operation between the activation a₁ (bit value a₁=F) andthe weight w₁ (bit value w₁=C), in a synapse in which the activation a₁and the weight w₁ are provided to cross each other. Similarly, in the3x3 virtual synapse array 740, bit values of all the first through thirdintermediate products 733 through 735 may be mapped as illustrated inFIG. 7D by performing operations in synapses in which the axons a₃, a₂,and a₁ and the weights w₃, w₂, and w₁ are provided to cross each other.

The controller circuitry 1124 determines activations and weights to beprovided to the axon circuitry 1121 and the synapse circuitry 1122 suchthat mapping is performed in the above manner of the virtual synapsearray 740.

FIGS. 8A and 8B are diagrams for describing an order in which operationsare to be performed by a neural network processor in a time-divisionmanner to process an operation, according to some example embodiments.

Referring to FIG. 8A, operations are performed between activations a₁,a₂, and a₂ and weights w₁, w₂, and w₂ at synapses at intersections ofthe activations a₁, a₂, and a₂ and the weights w₁, w₂, and w₂ in avirtual synapse array 810. The neural network processing circuitry 112may perform the operations at the intersections of the virtual synapsearray 810 in the time-division manner.

According to the mapping method described above with reference to FIGS.7A and 7D, at a at point of time t₀, the neural network processingcircuitry 112 provides the axon a₁ to the axon circuitry 1121 and thesynaptic weight w₁ to the synapse circuitry 1122 and obtains anoperation value between the axon a₁ and the synaptic weight w₁. Next, ata point of time t₁, the neural network processing circuitry 112 providesthe activation a₁ to the axon circuitry 1121 and the weight w₂ to thesynapse circuitry 1122 and obtains a synaptic operation value betweenthe axon a₁ and the synaptic weight w₂. In a similar manner, the neuralnetwork processing circuitry 112 sequentially performs operations up toa point of time t₈.

Referring to FIG. 8B, when the order described above with reference toFIG. 8A is indicated by arrows, it may be understood that operations areperformed at the point of time t₀ to the point of time t₈ in an order ofdiagonal directions in the virtual synapse array 810. Accordingly, inthe example of FIG. 8B, this order is based on a method by which thevalues of i and j are chosen and mapped by the controller circuitry1124, such that combinations from a combination of the i^(th) bit andthe j^(th) bit mapped such that the sum of the values i and j issmallest to a combination of the i^(th) bit to the j^(th) bit mappedsuch that the sum of the values i and j is largest are sequentiallyassigned to each of the axon circuitry 1121 and the synapse circuitry1122 as described above.

According to some example embodiments, the neural network processingcircuitry 112 assigns some bit values of operands to the activations andthe weights of the virtual synapse array 810 and performs operations inthe order of diagonal directions of the FIG. 8B in the time-divisionmanner, and thus is capable of performing an operation unlike a neuralnetwork processor of the related art.

FIG. 8C is a diagram for describing in detail a process of performing anoperation by a neural network processor in the time-division manner,according to some example embodiments.

A process of performing an operation by control circuitry 820 and addercircuitry 830 of the neuron circuitry 1123 will be described using theexamples, which are described above with reference to FIGS. 7A to 8B,below with reference to FIG. 8C.

At a point of time t₀, the controller circuitry 1124 assigns anactivation a₁ to the neuron circuitry 1123 and a weight w₁ to thesynapse circuitry 1122. When the weight w₁ is stored, the synapsecircuitry 1122 performs an operation on the activation a₁ and the weightw₁ to obtain an operation value corresponding to point of time t₀.

At the point of time t₀, the control circuitry 820 receivespredetermined initial values and an operation value corresponding to thepoint of time t₀, perform a determination whether the addition operationis to be performed by the adder circuitry 830, based on the receivedvalues, and outputs a positive determination or a negative determination(such as an enable signal or a disable signal), as a result of thedetermination.

At the point of time t₀, the adder circuitry 830 may receive thepositive determination (such as the enable signal) from the controlcircuitry 820 and thus perform the addition operation. For example, theadder circuitry 830 receives a predetermined initial value “0” as anaugend, receives the operation value corresponding to the point of timet₀ as an addend, and receives the predetermined initial value “0” as aprevious carry value. When receiving all inputs, the adder circuitry 830performs the addition operation and outputs an addition value S₀ and acarry value C₀. The addition value S₀ corresponds to an LSB among bitsof an operation value of an operation (e.g., a variable-resolutionmultiplication operation). The carry value C₀ is input as a previouscarry value of the addition operation to be performed next.

When receiving the negative determination (such as the disable signal)from the control circuitry 820, the adder circuitry 830 may skip theaddition operation at the point of time t₀. In this case, the receivingof the negative determination (such as the disable signal) from thecontrol circuitry 820 by the adder circuitry 830 means that theoperation value corresponding to the point of time t₀ is “0” and thusboth an addition value and a carry value that would be obtained from theadder circuitry 830 as if the addition operation were not skipped may be“0”. Even when the addition operation is not performed by the addercircuitry 830, the neuron circuitry 1123 may determine the additionvalue S₀ and the carry value C₀ to “0”, thereby reducing unnecessarypower consumption. It is to be appreciated that some example embodimentsmay perform various types of arithmetic and/or logical processing, andmay produce an arithmetic value such as a sum, difference, product,and/or dividend, in addition to a carry value.

At a point of time t₁, the controller circuitry 1124 assigns theactivation a₁ to the neuron circuitry 1123 and assigns a weight w₂ tothe synapse circuitry 1122. When the weight w₂ is stored, the synapsecircuitry 1122 performs an operation on the activation a₁ and the weightw₂ to obtain an operation value corresponding to the point of time t₁.

At a point of time t₂, the controller circuitry 1124 assigns anactivation a₂ to the neuron circuitry 1123 and the weight w₁ to thesynapse circuitry 1122. When the weight w₁ is stored, the synapsecircuitry 1122 performs an operation on the activation a₂ and the weightw₁ to obtain an operation value corresponding to the point of time t₂.

When receiving all inputs such as the carry value C₀, the operationvalue corresponding to the point of time t₁, and the operation valuecorresponding to the point of time t₂, the control circuitry 820performs a determination whether the addition operation is to beperformed by the adder circuitry 830, based on the received values, andoutputs the positive determination or the negative determination (suchas the enable signal or the disable signal) as a result of thedetermination.

When receiving the positive determination (such as the enable signal)from the control circuitry 820, the adder circuitry 830 may perform theaddition operation. For example, the adder circuitry 830 performs theaddition operation and outputs an addition value S₁ and a carry value C₁when receiving all inputs, such as the carry value C₀, the operationvalue corresponding to the point of time t₁, and the operation valuecorresponding to the point of time t₂. The addition value S₁ correspondsto a bit value of a second bit among the bits indicating the operationvalue of the operation (e.g., a variable-resolution multiplicationoperation). The carry value C₁ is input as a previous carry value of theaddition operation to be performed next. When receiving the negativedetermination (such as the disable signal) from the control circuitry820, the adder circuitry 830 may skip the addition operation at thepoint of time t₂. It would be obvious to those of ordinary skill in theart that when the addition operation to be performed by the addercircuitry 830 is skipped, an addition value or a carry value necessaryto perform the addition operation at a subsequent point of time or todetermine one of the bits indicating the operation value of theoperation may be appropriately determined, based on the abovedescription related to the operation at the point of time t₀.

At a point of time t₃, the controller circuitry 1124 assigns theactivation a₁ to the neuron circuitry 1123 and a weight w₃ to thesynapse circuitry 1122. The synapse circuitry 1122 obtains an operationvalue corresponding to the point of time t₃. At a point of time t₄, thecontroller circuitry 1124 assigns the activation a₂ to the neuroncircuitry 1123 and the weight w₂ to the synapse circuitry 1122. Thesynapse circuitry 1122 obtains an operation value corresponding to thepoint of time t₄.

When receiving all inputs such as the carry value C₁, the operationvalue corresponding to the point of time t₃, and the operation valuecorresponding to the point of time t₄, the control circuitry 820performs a determination whether the addition operation is to beperformed by the adder circuitry 830, based on the received values, andoutputs the positive determination or the negative determination (suchas the enable signal or the disable signal) as a result of thedetermination.

When receiving the positive determination (such as the enable signal)from the control circuitry 820, the adder circuitry 830 may perform theaddition operation. For example, when receiving all inputs such as thecarry value C₁, the operation value corresponding to the point of timet₃, and the operation value corresponding to the point of time t₄, theadder circuitry 830 performs the addition operation and outputs anaddition value P₀ and a carry value C₂. The addition value P₀ is used asan input for the adder circuitry 830 to perform the addition operationto be performed next at a point of time t₅. The carry value C₂ is inputas a previous carry value for the addition operation to be performednext. When receiving the negative determination (such as the disablesignal) from the control circuitry 820, the adder circuitry 830 may skipthe addition operation at the point of time t₄.

At the point of time t₅, the controller circuitry 1124 assigns anactivation a₃ to the neuron circuitry 1123 and the weight w₁ to thesynapse circuitry 1122. The synapse circuitry 1122 obtains an operationvalue corresponding to the point of time t₅.

When receiving all inputs such as the operation value corresponding tothe point of time t₅, the previously obtained addition value P₀, and thepredetermined carry value “0”, the control circuitry 820 performs adetermination whether an addition value is to be performed by the addercircuitry 830, based on the received values, and outputs the positivedetermination or the negative determination (such as the enable signalor the disable signal) as a result of the determination.

When receiving the positive determination (such as the enable signal)from the control circuitry 820, the adder circuitry 830 may perform theaddition operation. For example, when receiving all inputs such as theoperation value corresponding to the point of time t₅, the previouslyobtained addition value P₀, and the predetermined carry value “0”, theadder circuitry 830 performs the addition operation and outputs anaddition value S₂ and a carry value C₃. The addition value S₂corresponds to a bit value of a third bit among the bits indicating theoperation value of the operation (e.g., a variable-resolutionmultiplication operation). The carry value C₃ is input as a previouscarry value of the addition operation to be performed next. Whenreceiving the negative determination (such as the disable signal) fromthe control circuitry 820, the adder circuitry 830 may skip the additionoperation at the point of time t₅.

At point of time t₆, the controller circuitry 1124 assigns theactivation a₂ to the neuron circuitry 1123 and the weight w₃ to thesynapse circuitry 1122. The synapse circuitry 1122 obtains an operationvalue corresponding to the point of time t₆.

When receiving all inputs such as the operation value corresponding tothe point of time t₆, the predetermined initial value “0” and the carryvalue C₂, the control circuitry 820 performs a determination whether theaddition operation is to be performed by the adder circuitry 830, basedon the received values, and outputs the positive determination or thenegative determination (such as the enable signal or the disable signal)as a result of the determination.

When receiving the positive determination (such as the enable signal)from the control circuitry 820, the adder circuitry 830 may perform theaddition operation. For example, when receiving all inputs such as theoperation value corresponding to the point of time t₆, the predeterminedinitial value “0” and the carry value C₂, the adder circuitry 830performs the addition operation and outputs an addition value P₁ and acarry value C₄. The addition value P₁ is used as an input for the addercircuitry 830 to perform the addition operation next at a point of timet₇. The carry value C₄ is input as a previous carry value for theaddition operation to be performed next. When receiving the negativedetermination (such as the disable signal) from the control circuitry820, the adder circuitry 830 may skip the addition operation at thepoint of time t₆.

At the point of time t₇, the controller circuitry 1124 assigns theactivation as to the neuron circuitry 1123 and the weight w₂ to thesynapse circuitry 1122. The synapse circuitry 1122 obtains an operationvalue corresponding to the point of time t₇.

When receiving all inputs such as the operation value corresponding tothe point of time t₇, the previously obtained addition value P₁, and thecarry value C₃, the control circuitry 820 determines whether theaddition operation is to be performed by the adder circuitry 830, basedon the received values, and outputs the positive determination or thenegative determination (such as the enable signal or the disable signal)as a result of the determination.

When receiving the positive determination (such as the enable signal)from the control circuitry 820, the adder circuitry 830 may perform theaddition operation. For example, when receiving all inputs such as theoperation value corresponding to the point of time t₇, the previouslyobtained addition value P₁ and the carry value C₃, the adder circuitry830 performs the addition operation and outputs an addition value S₃ andthe carry value C₅. The addition value S₃ corresponds to a bit value ofa fourth bit among the bits of the operation value of the operation(e.g., the variable-resolution multiplication operation). The carryvalue C₅ is input as a previous carry value for the addition operationto be performed next. When receiving the negative determination (such asthe disable signal) from the control circuitry 820, the adder circuitry830 may skip the addition operation at the point of time t₇.

At a point of time t₈, the controller circuitry 1124 assigns theactivation as to the neuron circuitry 1123 and the weight w₃ to thesynapse circuitry 1122. The synapse circuitry 1122 obtains an operationvalue corresponding to the point of time t₈.

When receiving all inputs such as the operation value corresponding tothe point of time t₈, the carry value C₄ and the carry value C₅, thecontrol circuitry 820 performs a determination whether the additionoperation is to be performed by the adder circuitry 830, based on thereceived values, and outputs the positive determination or the negativedetermination (such as the enable signal or the disable signal) as aresult of the determination.

When receiving the positive determination (such as the enable signal)from the control circuitry 820, the adder circuitry 830 may perform theaddition operation. For example, when receiving all inputs such as theoperation corresponding to the point of time t₈, the carry value C₄ andthe carry value C₅, the adder circuitry 830 performs the additionoperation and outputs an addition value S₄ and a carry value S₅. Theaddition values S₄ and S₅ respectively correspond to a bit value of afifth bit and a sixth bit among the bits of the operation value of theoperation (e.g., a variable-resolution multiplication operation). Assuch, S₅S₄S₃S₂S₁S₀ may be produced as the operation value of themultiplication operation by sequentially performing operations at thepoint of time t₀ to the point of time t₈.

As described above, at each point of time, the control circuitry 820 ofthe neuron circuitry 1123 may be configured to perform a determinationwhether the addition operation is to be performed by the adder circuitry830, and output the positive determination or the negative determination(such as the enable signal or the disable signal) as a result of thedetermination. An unnecessary operation to be performed by the controlcircuitry 820 may be skipped by the adder circuitry 830, and thus, powerconsumption for driving the neural network processing circuitry 112 maybe reduced.

The adder circuitry 830 may be reused to perform the addition operationat each point of time. For example, the adder circuitry 830 may receivethe positive determination (such as the enable signal) from the controlcircuitry 820, perform the addition operation when all inputs arereceived, and store previous operation values (e.g., arithmetic and/orlogical values such as addition values, carry values, etc.) in a memory(a buffer, a register, or the like) connected to the adder circuitry 830to perform a next addition operation. The previous addition operationvalues stored in the memory may be reset and reused before a nextaddition operation is performed. That is, in some example embodiments,the neural network processing circuitry 112 may be configured to performthe multiplication operation by reusing the adder circuitry 830 and thuscircuitry area for realizing the neural network processing circuitry 112may be reduced.

FIG. 9 is a diagram illustrating an example of a process of processing a3-bitx3-bit multiplication operation by a neural network processor,according to some example embodiments.

FIG. 9 illustrates a virtual synapse array 920 for performing avariable-resolution multiplication operation 910 on a 3-bit weight 001 ₂and a 3-bit activation 010 ₂, and a sequential operation process 930 ofperforming the multiplication operation 910. When the multiplicationoperation 910 is performed on the 3-bit weight 001 ₂ and the 3-bitactivation 010 ₂ in the above-described manner, 000010₂ which is a 6-bitmultiplication result may be produced as an operation value of themultiplication operation.

During the process, six addition operations among a total of sevenaddition operations to be performed by the adder circuitry 830 may beskipped by the control circuitry 820 and thus power consumption neededto drive the adder circuitry 830 may be reduced by about 86%. It will beobvious to those of ordinary skill in the art that an effect of reducingpower consumption by skipping unnecessary operations may vary accordingto a weight and an activation but it may be more efficient on averagewhen unnecessary operations are skipped than when the unnecessaryoperations are not skipped.

For convenience of explanation, a 3-bit×3-bit multiplication operationhas been described as an example of a variable-resolution operation.However, it would be obvious to those of ordinary skill in the art thatthe neural network processing circuitry 112 may also perform themultiplication operation on other various bits through a distributionand time-division processing of bit values of an activation and aweight.

FIG. 10 is a flowchart of a method of processing an operation by aneural network apparatus, according to some example embodiments.

Referring to FIG. 10 , the method of processing the operation by theneural network apparatus includes operations processed by the neuralnetwork apparatus as described above in a time-series manner.Accordingly, the above description with reference to the drawings, evenwhen not given below, is also applicable to the method of FIG. 10 .

In operation 1010, the controller circuitry 1124 of the neural networkapparatus 100 determines a first input corresponding to an i^(th) bit ofan n-bit activation to be assigned to the axon circuitry 1121 at eachpoint of time, and a second input corresponding to a j^(th) bit of anm-bit weight to be assigned to the synapse circuitry 1122 at each pointof time. In this case, the controller circuitry 1124 may repeatedlydetermine a first input and a second input to be assigned at each pointof time until an operation value of the operation is producedsequentially from a bit value to an upper bit value.

In operation 1020, the axon circuitry 1121 receives the determined firstinput.

In operation 1030, the synapse circuitry 1122 outputs a value of anoperation performed between the first input and the second input whenthe determined second input is stored.

In operation 1040, the control circuitry 820 of the neuron circuitry1123 may perform a determination whether an addition operation is to beperformed on, as an input, at least one of operation values output fromthe synapse circuitry 1122 to produce an operation value of theoperation performed between the n-bit activation and the m-bit weight,and may output the positive determination or the negative determination(such as the enable signal or the disable signal) as a result of thedetermination.

In operation 1050, when receiving the positive determination (such asthe enable signal), the adder circuitry 830 of the neuron circuitry 1123may perform the addition operation and obtain one of bits of anoperation value of the operation. When receiving the negativedetermination (such as the disable signal) from the control circuitry820, the adder circuitry 830 may skip the addition operation.

FIG. 11 is a block diagram of a configuration of an electronic system1100 according to some example embodiments.

Referring to FIG. 11 , the electronic system 1100 may extract validinformation by analyzing input data in real-time based on a neuralnetwork and determine a situation based on the extracted validinformation or control components of an electronic device on which theelectronic system 1100 is mounted. For example, the electronic system1100 is applicable to a robot apparatus, such as a drone or an advanceddrivers assistance system (ADAS), a smart TV, a smart phone, a medicaldevice, a mobile device, an image display device, a measuring device, anIoT device, etc., and may be mounted on at least one of various types ofelectronic devices.

The electronic system 1100 may include a processor 1110, RAM 1120, aneural network apparatus 1130, a memory 1140, a sensor module 1150, anda communication (Tx/Rx) module 1160. The electronic system 1100 mayfurther include an input/output module, a security module, a powercontrol device, etc. At least some of hardware components of theelectronic system 1100 may be mounted on at least one semiconductorchip.

The processor 1110 controls overall operations of the electronic system1100. The processor 1110 may include one processor core (core) or aplurality of processor cores (multi-core). The processor 1110 mayprocess or execute programs and/or data stored in the memory 1140. Insome example embodiments, the processor 1110 may execute the programsstored in the memory 1140 to control functions of the neural networkapparatus 1130. The processor 1110 may be a central processing unit(CPU), a graphics processing unit (GPU), an application processor (AP),or the like.

The RAM 1120 may temporarily store programs, data, or instructions. Forexample, the programs and/or data stored in the memory 1140 may betemporarily stored in the RAM 1120 under control of the processor 1110or according to booting code. The RAM 1120 may be embodied as a memorysuch as DRAM or SRAM.

The neural network apparatus 1130 may be configured to perform anoperation of a neural network based on received input data and generatean information signal based on the operation value of the operation,where the information signal may encode and/or be based on the operationvalue of the operation. The neural network may include a CNN, an RNN, anFNN, deep belief networks, restricted Boltzmann machines, etc. but isnot limited thereto. In some example embodiments, the neural networkapparatus 1130 may be a neural network-exclusive hardware accelerator ora device including the same, and may correspond to the neural networkapparatus 100 of FIG. 5 described above.

The information signal may include one of various types of recognitionsignals, such as a voice recognition signal, an object recognitionsignal, an image recognition signal, a biometric information recognitionsignal, etc. In some example embodiments, the neural network apparatus1130 may be configured to receive, as input data, frame data included ina video stream, and generate a recognition signal with respect to anobject included in an image indicated by the frame data from the framedata. However, some example embodiments may not be limited thereto; forexample, the neural network apparatus 1130 may be configured to receivevarious types of input data according to a type or function of anelectronic apparatus on which the electronic system 1100 is mounted, andto generate a recognition signal based on the input data.

The memory 1140 is a storage space for storing data, and may store anoperating system (OS), various programs, and various types of data. Insome example embodiments, the memory 1140 may be configured to storeintermediate results generated during performing an operation in theneural network apparatus 1130.

The memory 1140 may be DRAM but is not limited thereto. The memory 1140may include at least one of a volatile memory or a nonvolatile memory.Examples of the nonvolatile memory include ROM, PROM, EPROM, EEPROM, aflash memory, PRAM, MRAM, RRAM, FRAM, etc. Examples of the volatilememory include DRAM, SRAM, SDRAM, PRAM, MRAM, RRAM, FeRAM, etc. In someexample embodiments, the memory 1140 may include at least one of an HDD,an SSD, a CF, an SD, a Micro-SD, a Mini-SD, an xD, or a memory stick

The sensor module 1150 may collect surrounding information of anelectronic apparatus on which the electronic system 1100 is mounted. Thesensor module 1150 may sense or receive a signal (for example, an imagesignal, a voice signal, a magnetic signal, a biometric signal, or atouch signal) from the outside of the electronic apparatus, and convertthe sensed or received signal into data. To this end, the sensor module1150 may include at least one of various types of sensing devices, suchas a microphone, an image pickup device, an image sensor, a lightdetection and ranging (LIDAR) sensor, an ultrasound sensor, an infraredsensor, a bio-sensor, and a touch sensor.

The sensor module 1150 may provide, as input data, the data convertedfrom the sensed or received signal to the neural network apparatus 1130.For example, the sensor module 1150 may include an image sensor, and maygenerate a video stream by photographing an external environment of theelectronic apparatus and sequentially provide consecutive data frames ofthe video stream as input data to the neural network apparatus 1130.However, some example embodiments may not be limited thereto; forexample, the sensor module 1150 may provide various types of data to theneural network apparatus 1130.

The communication (Tx/Rx) module 1160 may include various wired orwireless interfaces to communicate with an external device. For example,the communication (Tx/Rx) module 1160 may include communicationinterfaces to connect to a local area network (LAN), a wireless LAN(WLAN) such as wireless fidelity (Wi-Fi), a wireless personal areanetwork (WPAN) such as Bluetooth, wireless universal serial bus (USB),ZigBee, near-field communication (NFC), radio-frequency identification(RFID), power line communication (PLC), a mobile cellular network suchas 3^(rd) generation (3G), 4^(th) generation (4G), or long-termevolution (LTE), etc.

The above-described example embodiments of the present disclosure may beembodied as computer programs and implemented in general-use digitalcomputers capable of executing the programs via a computer-readablerecording medium. Data structures such as those employed in the exampleembodiments may be recorded on a computer-readable recording mediumthrough various means. Examples of the computer-readable recordingmedium include magnetic storage media (e.g., ROM, floppy disks, harddisks, etc.), optical recording media (e.g., CD-ROMs, DVDs, etc.), andso on.

While the present disclosure has been described above with respect tosome example embodiments thereof, it will be understood by those ofordinary skill in the art that various changes in form and details maybe made therein without departing from the spirit and scope of thepresent disclosure. Accordingly, the example embodiments set forthherein should be considered in a descriptive sense only and not forpurposes of limitation. The scope of the present disclosure is definednot by the foregoing description but by the appended claims, and allmodifications within the scope of equivalents should be construed asbeing included in the scope of the present disclosure.

What is claimed is:
 1. A neural network apparatus that processes anoperation, the neural network apparatus comprising: neural networkcircuitry configured to, map i and j such that an i^(th) bit of an n-bitdata and a j^(th) bit of an m-bit data are differently combined forrespective instances of performing the operation, wherein n is a naturalnumber and i is a natural number between 1 and n inclusive, and whereinm is a natural number and j is a natural number between 1 and minclusive; receive, for each of the respective instances, a first inputcorresponding to the i^(th) bit of the n-bit data; receive, for each ofthe respective instances, a second input corresponding to the j^(th) bitof the m-bit data; perform, for each of the respective instances, adetermination whether the operation is to be performed on the i^(th) bitand the j^(th) bit; and based on the determination being a positivedetermination, perform the operation on the i^(th) bit and the j^(th)bit, and produce each bit of an operation value of the operationperformed on the i^(th) bit and the j^(th) bit based on thedetermination.
 2. The neural network apparatus of claim 1, wherein theneural network circuitry is configured to perform the determinationbased on whether another operation may be influenced by whether theoperation is performed on, as an input, the operation value of theoperation, and wherein the determination is a negative determinationbased on the other operation not being influenced by the operation valueof the operation.
 3. The neural network apparatus of claim 2, wherein,the operation includes an arithmetic operation, the neural networkcircuitry is configured to not perform the arithmetic operation when thedetermination is the negative determination, and the neural networkcircuitry is configured to produce the operation value of the operationincluding an arithmetic value and a carry value by performing theoperation on the i^(th) bit and the j^(th) bit based on thedetermination.
 4. The neural network apparatus of claim 1, wherein theneural network circuity is configured to, receive a set of receivedvalues including at least one of, a predetermined initial value, a firstoperation value produced at a previous point of time, a second operationvalue produced at a current point of time, an arithmetic value producedby the neural network circuitry at the previous point of time, and acarry value produced by the neural network circuitry at the previouspoint of time, and perform the determination as, a negativedetermination based on the received values are the same as the firstoperation value, and the positive determination based on any one of thereceived values being the same as the second operation value.
 5. Theneural network apparatus of claim 1, wherein the neural networkcircuitry further comprises a full adder configured to perform theoperation on, as inputs, a predetermined initial value, a firstoperation value produced by the neural network circuitry at a previouspoint of time, a second operation value produced by the neural networkcircuitry at a current point of time, an arithmetic value produced bythe neural network circuitry at the previous point of time, and a carryvalue produced by the neural network circuitry at the previous point oftime.
 6. The neural network apparatus of claim 1, wherein at least oneof an arithmetic value or a carry value produced by the neural networkcircuitry corresponds to a bit value of a set of bits produced by theoperation.
 7. The neural network apparatus of claim 1, wherein, after abit of the operation value of the operation is produced, the neuralnetwork circuitry is configured to perform the operation again toproduce another bit of the operation value of the operation.
 8. Theneural network apparatus of claim 1, wherein the neural networkcircuitry is further configured to determine the first inputcorresponding to the i^(th) bit and the second input corresponding tothe j^(th) bit to sequentially obtain a lower bit value to an upper bitvalue of the operation value of the operation.
 9. The neural networkapparatus of claim 1, wherein the neural network circuitry is furtherconfigured to choose i and j such that combinations, ranging from acombination of the i^(th) bit and the j^(th) bit such that a sum of iand j is smallest to a combination of the i^(th) bit and the j^(th) bitsuch that a sum of i and j is largest, are sequentially performed by theneural network circuitry.
 10. The neural network apparatus of claim 1,wherein the n-bit data is an n-bit activation and the m-bit data is anm-bit weight.
 11. A method of processing an operation by a neuralnetwork apparatus, the method comprising: determining a first inputcorresponding to an i^(th) bit of an n-bit data, and a second inputcorresponding to a j^(th) bit of an m-bit data; performing adetermination whether the operation is to be performed on the firstinput and the second input, and produce a positive determination or anegative determination as a result of the determination; and performingthe operation on the first input and the second input to produce anoperation value of the operation based on the positive determination,wherein n and m are natural numbers, i is a natural number between 1 andn inclusive, and j is a natural number between 1 and m inclusive, andwherein producing the first input and the second input includes mappingi and j such that the i^(th) bit and the j^(th) bit are differentlycombined for respective instances of the operation.
 12. The method ofclaim 11, wherein the positive determination is based on when theoperation value of the operation may be influenced by the operationbeing performed on, as an input, at least one of the operation valuesproduced by a preceding instance of the operation, and the negativedetermination is based on when the operation output of the operation maynot be influenced by the operation value produced by the precedinginstance of the operation.
 13. The method of claim 12, furthercomprising: skipping the operation based on the negative determination,and determining an addition value and a carry value that are to beobtained based on the positive determination.
 14. The method of claim11, wherein the determination of the positive determination or thenegative determination is based on, receiving at least one of apredetermined initial value, an operation value produced at a previouspoint of time, an operation value produced at a current point of time,an addition value produced by at the previous point of time, and/or acarry value produced at the previous point of time; performing thepositive determination based on the received values being the same as afirst value; and performing the negative determination when any one ofthe received values is the same as a second value.
 15. The method ofclaim 11, wherein the producing of one of the bits of the operationvalue of the operation includes performing the operation on, as inputs,a predetermined initial value, an operation value produced at a previouspoint of time, an operation value produced at a current point of time,an addition value produced at the previous point of time, and a carryvalue produced at the previous point of time.
 16. The method of claim11, wherein at least one of an addition value or a carry valuecorresponds to a bit value of one of bits of the operation value of theoperation.
 17. The method of claim 11, wherein the producing of one ofthe bits of the operation value of the operation includes, when a bitvalue of one of the bits of the operation value of the operation isproduced, performing the operation again to produce another bit of theoperation value of the operation.
 18. The method of claim 11, whereinthe mapping of i and j comprises choosing i and j such thatcombinations, ranging from a combination of the i^(th) bit and thej^(th) bit such that a sum of i and j is smallest to a combination ofthe i^(th) bit and the j^(th) bit such that a sum of i and j is largest,are sequentially performed.
 19. The method of claim 11, wherein then-bit data is an n-bit activation and the m-bit data is an m-bit weight.20. A non-transitory computer-readable recording medium having recordedthereon a program that, when executed by a computer, performs the methodof claim 11.